Name
Abstract | ||
|---|---|---|
The Database-as-a-Service (DAS) model allowing users to outsource data to the clouds has been a promising paradigm. Since users’ data may contain private information and the cloud servers may not be fully trusted, it is desirable to encrypt the data before outsourcing and as a result, the functionality and efficiency has to be sacrificed. In this paper, we propose a privacy-enhancing range query processing scheme by utilizing polynomials and kNN technique. We prove that our scheme is secure under the widely adopted honest-but-curious model and the known background model. Since the secure indexes and trapdoors are indistinguishable and unlinkable, the data privacy can be protected even when the cloud server possesses additional information, such as the attribute domain and the distribution of this domain. In addition, results of experiments validating our proposed scheme are also provided. |
Year | Venue | Field |
|---|---|---|
2015 | WISE | Web search query,Query expansion,Computer science,Range query (data structures),Server,Information privacy,Database,Attribute domain,Cloud database,Cloud computing |
DocType | Citations | PageRank |
Conference | 1 | 0.35 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jialin Chi | 1 | 4 | 1.10 |
| Cheng Hong | 2 | 12 | 2.59 |
| Min Zhang | 3 | 134 | 38.40 |
| zhenfeng | 4 | 40 | 6.18 |